2027 Applied Science Internship - Natural Language Processing and Speech Technologies - United States, PhD Student Science Recruiting
AI in this role
Research and develop novel NLP, speech recognition, and conversational AI algorithms as an Applied Science Intern.
Are you a master of natural language processing, eager to push the boundaries of conversational AI? Amazon is seeking exceptional graduate students to join our cutting-edge research team, where they will have the opportunity to explore and push the boundaries of natural language processing (NLP), natural language understanding (NLU), and speech recognition technologies.
Imagine waking up each morning, fueled by the excitement of tackling complex research problems that have the potential to reshape the world. You'll dive into production-scale data, exploring innovative approaches to natural language understanding, large language models, reinforcement learning with human feedback, conversational AI, and multimodal learning. Your days will be filled with brainstorming sessions, coding sprints, and lively discussions with brilliant minds from diverse backgrounds.
Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated..
Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology.
Amazon has positions available for Natural Language Processing & Speech Applied Science Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA.
Key job responsibilities
We are particularly interested in candidates with expertise in: NLP/NLU, LLMs, Reinforcement Learning, Human Feedback/HITL, Deep Learning, Speech Recognition, Conversational AI, Natural Language Modeling, Multimodal Learning.
In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more.
The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems and to communicate research findings clearly. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment.
Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. Critically review and validate outputs from AI tools and automated systems.
A day in the life
- Develop novel, scalable algorithms and modeling techniques that advance the state-of-the-art in natural language processing, speech recognition, text-to-speech, question answering, and conversational modeling.
- Tackle groundbreaking research problems on production-scale data, leveraging techniques such as LSTM, transformer-based models, signal processing, information extraction, audio processing, speaker detection, large language models, and multilingual modeling.
- Collaborate with cross-functional teams to solve complex business problems, leveraging your expertise in NLP/NLU, LLMs, reinforcement learning, human feedback/HITL, deep learning, speech recognition, conversational AI, natural language modeling, and multimodal learning.
- Thrive in a fast-paced, ever-changing environment, embracing ambiguity and demonstrating strong attention to detail.
Basic qualifications
- Are enrolled in a PhD
- Can relocate to where the internship is based
- Experience programming in Python, and where applicable, C++ or Java
- Experience with one or more of the following: Natural Language Processing/Understanding, Large Language Models, Reinforcement Learning, Human Feedback/HITL, Deep Learning, Speech Recognition, Conversational AI, Natural Language Modeling, Multimodal Learning
- Must be available for full-time (40 hours per week) internship for the whole duration of the internship
- Demonstrated science depth in a specific research area, evidenced through publications, thesis work, or equivalent contributions
Preferred qualifications
- Have publications at top-tier peer-reviewed conferences or journals
- Experience in designing experiments and statistical analysis of results
- Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
- Experience implementing algorithms using modern NLP and speech toolkits (e.g. PyTorch, HuggingFace, speech and audio processing frameworks)
- Experience with AI-assisted development tools to accelerate research, coding, or evaluation
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The starting pay for this position is listed below. Starting Day 1 of employment, Amazon offers EAP, Mental Health Support, Medical Advice Line, 401(k) matching. Learn more about our benefits at https://hiring.amazon.com/why-amazon/benefits.
Seattle, WA, USA - 135,660.00 USD Annually
How we rate this
2027 Applied Science Internship - Natural Language Processing and Speech Technologies - United States, PhD Student Science Recruiting at Amazon rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- What have you built with speech recognition or text-to-speech, and where did it break?
- Tell me about a project where natural language processing was part of your work. What did you do?
- Tell me about a project where speech recognition was part of your work. What did you do?
- Tell me about a project where reinforcement learning was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: NLP, Speech, Natural Language Processing, Speech Recognition, and Reinforcement Learning. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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